**This has been replaced with the music analysis now built in to music_monitor [here](https://github.com/benarmstead/music_monitor).**
Generates graphs on users music listening habits.
This is meant for processing and analysing the data from my cmus-music-monitor shell script.
However, this program will work on any CSV formatted:
<Title>, <Artist>, <Album>, <Genre>, <Song Length>, <Track number>, <Year>, <Play date>, <Play time>, <Volume>
(Its also very easy to modify it to take data from another format if you know python).
- Move to a database format rather than CSV (need to convert cmus-music-monitor to this first though).
- Generate predicted usage and spot patterns with machine learning.
- Implement more features such as different ways of viewing graphs etc.
git clone https://github.com/benarmstead/music-grapher
python3 main.py /path/to/your.csv
Feature Text Explinations
- Ability to genearte a video of a pie chart changing over time based on your music listening habits.
(Cannot demonstrate here due to being unable to remove band names from my example video)
(It is essentially Most Played Artists however animated over time)
Feature Image Examples
Most Played Songs
Most Played Artists
Avg songs p/day
Most Played Days
Unique songs played